An Empirical Comparison of Tree-Based Learning Algorithms: An Egyptian Rice Diseases Classification Case Study

Applications of learning algorithms in knowledge discovery are promising and relevant area of research. The classification algorithms of data mining have been successfully applied in the recent years to predict Egyptian rice diseases. Various classification algorithms can be applied on such data to devise methods that can predict the occurrence of diseases. However, the accuracy of such techniques differ according to the learning and classification rule used. Identifying the best classification algorithm among all available is a challenging task. In this study, a comprehensive comparative analysis of a tree-based different classification algorithms and their performance has been evaluated by using Egyptian rice diseases data set. The experimental results demonstrate that the performance of each classifier and the results indicate that the decision tree gave the best results.

@article{El-Telbany2016,
  title     = {An Empirical Comparison of Tree-Based Learning Algorithms: An Egyptian Rice Diseases Classification Case Study},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {5},
  number    = {1},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Mohammed E. El-Telbany and Mahmoud Warda},
  doi       = {10.14569/IJARAI.2016.050104},
  url       = {https://doi.org/10.14569/IJARAI.2016.050104}
}